Biomimetics

33 pages, 1615 KiB

Open AccessArticle

An Enhanced Artificial Lemming Algorithm and Its Application in UAV Path Planning

by Xuemei Zhu, Chaochuan Jia, Jiangdong Zhao, Chunyang Xia, Wei Peng, Ji Huang and Ling Li

Biomimetics 2025, 10(6), 377; https://doi.org/10.3390/biomimetics10060377 - 6 Jun 2025

This paper presents an enhanced artificial lemming algorithm (EALA) for solving complex unmanned aircraft system (UAV) path planning problems in three-dimensional environments. Key improvements include chaotic initialization, adaptive perturbation, and hybrid mutation, enabling a better exploration–exploitation balance and local refinement. Validation on the [...] Read more.

This paper presents an enhanced artificial lemming algorithm (EALA) for solving complex unmanned aircraft system (UAV) path planning problems in three-dimensional environments. Key improvements include chaotic initialization, adaptive perturbation, and hybrid mutation, enabling a better exploration–exploitation balance and local refinement. Validation on the IEEE CEC2017 and CEC2022 benchmark functions demonstrates the EALA’s superior performance, achieving faster convergence and better algorithm performance compared to the standard ALA and 10 other algorithms. When applied to UAV path planning in large- and medium-scale environments with realistic obstacle constraints, the EALA generates Pareto-optimal paths that minimize length, curvature, and computation time while guaranteeing collision avoidance. Benchmark tests and realistic simulations show that the EALA outperforms 10 algorithms. This method is particularly suited for mission-critical applications with strict safety and time constraints. Full article

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12 pages, 3316 KiB

Open AccessArticle

A Design Process Framework and Tools for Teaching and Practicing Biomimicry

by Benjamin Linder and Jean Huang

Biomimetics 2025, 10(6), 376; https://doi.org/10.3390/biomimetics10060376 - 6 Jun 2025

Abstract

Few design methods exist that provide clearly structured, visually intuitive, and easily monitored scaffolding for navigating the considerable complexity of biomimetic processes. To this end, we present a holistic biomimicry process framework informed by design abstraction models that clarifies core skills involved and [...] Read more.

Few design methods exist that provide clearly structured, visually intuitive, and easily monitored scaffolding for navigating the considerable complexity of biomimetic processes. To this end, we present a holistic biomimicry process framework informed by design abstraction models that clarifies core skills involved and how they combine to form essential practices, such as biology-to-design and challenge-to-biology. The structure–function and conditions-conducive-to-life perspectives are facilitated, ensuring support for sustainability considerations. We pair this framework with two process tools that make biomimicry design moves explicit for learners, educators, and practitioners. We share examples of the use of these tools from an upper-level undergraduate engineering course in biomimicry. Our observations indicate that the framework and tools support process acquisition, design iteration, knowledge transfer, and sustainability integration in biomimicry education and practice by enabling common design behaviors such as variation, iteration, debugging, and reflection. Full article

(This article belongs to the Special Issue Biomimetics—A Chance for Sustainable Developments: 2nd Edition)

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23 pages, 1999 KiB

Open AccessReview

Multi-Agent Reinforcement Learning in Games: Research and Applications

by Haiyang Li, Ping Yang, Weidong Liu, Shaoqiang Yan, Xinyi Zhang and Donglin Zhu

Biomimetics 2025, 10(6), 375; https://doi.org/10.3390/biomimetics10060375 - 6 Jun 2025

Abstract

Biological systems, ranging from ant colonies to neural ecosystems, exhibit remarkable self-organizing intelligence. Inspired by these phenomena, this study investigates how bio-inspired computing principles can bridge game-theoretic rationality and multi-agent adaptability. This study systematically reviews the convergence of multi-agent reinforcement learning (MARL) and [...] Read more.

Biological systems, ranging from ant colonies to neural ecosystems, exhibit remarkable self-organizing intelligence. Inspired by these phenomena, this study investigates how bio-inspired computing principles can bridge game-theoretic rationality and multi-agent adaptability. This study systematically reviews the convergence of multi-agent reinforcement learning (MARL) and game theory, elucidating the innovative potential of this integrated paradigm for collective intelligent decision-making in dynamic open environments. Building upon stochastic game and extensive-form game-theoretic frameworks, we establish a methodological taxonomy across three dimensions: value function optimization, policy gradient learning, and online search planning, thereby clarifying the evolutionary logic and innovation trajectories of algorithmic advancements. Focusing on complex smart city scenarios—including intelligent transportation coordination and UAV swarm scheduling—we identify technical breakthroughs in MARL applications for policy space modeling and distributed decision optimization. By incorporating bio-inspired optimization approaches, the investigation particularly highlights evolutionary computation mechanisms for dynamic strategy generation in search planning, alongside population-based learning paradigms for enhancing exploration efficiency in policy refinement. The findings reveal core principles governing how groups make optimal choices in complex environments while mapping the technological development pathways created by blending cross-disciplinary methods to enhance multi-agent systems. Full article

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33 pages, 12896 KiB

Open AccessArticle

A Bipedal Robotic Platform Leveraging Reconfigurable Locomotion Policies for Terrestrial, Aquatic, and Aerial Mobility

by Zijie Sun, Yangmin Li and Long Teng

Biomimetics 2025, 10(6), 374; https://doi.org/10.3390/biomimetics10060374 - 5 Jun 2025

Abstract

Biological systems can adaptively navigate multi-terrain environments via morphological and behavioral flexibility. While robotic systems increasingly achieve locomotion versatility in one or two domains, integrating terrestrial, aquatic, and aerial mobility into a single platform remains an engineering challenge. This work tackles this by [...] Read more.

Biological systems can adaptively navigate multi-terrain environments via morphological and behavioral flexibility. While robotic systems increasingly achieve locomotion versatility in one or two domains, integrating terrestrial, aquatic, and aerial mobility into a single platform remains an engineering challenge. This work tackles this by introducing a bipedal robot equipped with a reconfigurable locomotion framework, enabling seven adaptive policies: (1) thrust-assisted jumping, (2) legged crawling, (3) balanced wheeling, (4) tricycle wheeling, (5) paddling-based swimming, (6) air-propelled drifting, and (7) quadcopter flight. Field experiments and indoor statistical tests validated these capabilities. The robot achieved a 3.7-m vertical jump via thrust forces counteracting gravitational forces. A unified paddling mechanism enabled seamless transitions between crawling and swimming modes, allowing amphibious mobility in transitional environments such as riverbanks. The crawling mode demonstrated the traversal on uneven substrates (e.g., medium-density grassland, soft sand, and cobblestones) while generating sufficient push forces for object transport. In contrast, wheeling modes prioritize speed and efficiency on flat terrain. The aquatic locomotion was validated through trials in static water, an open river, and a narrow stream. The flight mode was investigated with the assistance of the jumping mechanism. By bridging terrestrial, aquatic, and aerial locomotion, this platform may have the potential for search-and-rescue and environmental monitoring applications. Full article

(This article belongs to the Section Locomotion and Bioinspired Robotics)

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34 pages, 12748 KiB

Open AccessArticle

An Improved Northern Goshawk Optimization Algorithm for Mural Image Segmentation

by Jianfeng Wang, Zuowen Bao and Hao Dong

Biomimetics 2025, 10(6), 373; https://doi.org/10.3390/biomimetics10060373 - 5 Jun 2025

Abstract

In the process of mural protection and restoration, using optimization algorithms for image segmentation is a common method for restoring mural details. However, existing optimization-based image segmentation methods often lack image segmentation quality. To alleviate the aforementioned issues, this paper proposes a mural [...] Read more.

In the process of mural protection and restoration, using optimization algorithms for image segmentation is a common method for restoring mural details. However, existing optimization-based image segmentation methods often lack image segmentation quality. To alleviate the aforementioned issues, this paper proposes a mural image segmentation algorithm based on OPBNGO by integrating the Northern Goshawk Optimization (NGO) algorithm with the off-center learning strategy, partitioned learning strategy, and Bernstein-weighted learning strategy. In OPBNGO, firstly, the off-center learning strategy is proposed, which effectively improves the global search ability of the algorithm by utilizing biased center individuals. Secondly, the partitioned learning strategy is introduced, which achieves a better balance between the exploration and development phases by applying diverse learning methods to the population. Finally, the Bernstein-weighted learning strategy is proposed, which effectively improves the algorithm’s development performance. Subsequently, the OPBNGO algorithm is applied to solve the image segmentation problem for eight mural images. Experimental results show that it achieves a winning rate of over 96.87% in terms of fitness function value, achieves a winning rate of over 93.75% in terms of FSIM, SSIM, and PSNR metrics, and can be considered a promising mural image segmentation algorithm. Full article

(This article belongs to the Special Issue Advances in Swarm Intelligence Optimization Algorithms and Applications: 2nd Edition)

17 pages, 11508 KiB

Open AccessArticle

Adaptive Neural Network Robust Control of FOG with Output Constraints

by Shangbo Liu, Baowang Lian, Jiajun Ma, Xiaokun Ding and Haiyan Li

Biomimetics 2025, 10(6), 372; https://doi.org/10.3390/biomimetics10060372 - 5 Jun 2025

Abstract

In this work, an adaptive robust control method based on Radial Basis Function Neural Network (RBFNN) is proposed. Inspired by the local response characteristics of biological neurons, this method can reduce the influence of nonlinear errors and unknown perturbations in the extreme working [...] Read more.

In this work, an adaptive robust control method based on Radial Basis Function Neural Network (RBFNN) is proposed. Inspired by the local response characteristics of biological neurons, this method can reduce the influence of nonlinear errors and unknown perturbations in the extreme working conditions of the aircraft, such as high dynamics and strong vibration, so as to achieve high tracking accuracy. In this method, the dynamic model of the nonlinear error of the fiber optic gyroscope is proposed, and then the unknown external interference observer is designed for the system to realize the estimation of the unknown disturbances. The controller design method combines the design of the adaptive law outside the finite approximation domain of the achievable condition design of the sliding mode surface, and adjusts the controller parameters online according to the conditions satisfied by the real-time error state, breaking through the limitation of the finite approximation domain of the traditional neural network. In the finite approximation domain, an online adaptive controller is constructed by using the universal approximation ability of RBFNN, so as to enhance the robustness to nonlinear errors and external disturbances. By designing the output constraint mechanism, the dynamic stability of the system is further guaranteed under the constraints, and finally its effectiveness is verified by simulation analysis, which provides a new solution for high-precision inertial navigation. Full article

(This article belongs to the Special Issue Advanced Biologically Inspired Vision and Its Application)

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Graphical abstract

20 pages, 5810 KiB

Open AccessArticle

The Effects of the Substrate Length and Cultivation Time on the Physical and Mechanical Properties of Mycelium-Based Cushioning Materials from Salix psammophila and Peanut Straw

by Xiaowen Song, Shuoye Chen, Jianxin Wu, Ziyi Cai, Yanfeng Zhang, Risu Na, He Lv, Cong He, Tingting Wu and Xiulun Wang

Biomimetics 2025, 10(6), 371; https://doi.org/10.3390/biomimetics10060371 - 5 Jun 2025

Abstract

Mycelium-based biocomposites represent a novel class of environmentally friendly materials. This study investigated the potential of using Salix psammophila and peanut straw as substrates for cultivating Pleurotus ostreatus and Ganoderma lucidum, respectively, to fabricate mycelium-based cushioning materials. The results demonstrated that the [...] Read more.

Mycelium-based biocomposites represent a novel class of environmentally friendly materials. This study investigated the potential of using Salix psammophila and peanut straw as substrates for cultivating Pleurotus ostreatus and Ganoderma lucidum, respectively, to fabricate mycelium-based cushioning materials. The results demonstrated that the Pleurotus ostreatus-based cushion material using Salix psammophila (POSM) outperformed the Ganoderma lucidum-based cushion material using peanut straw (GLPM) in terms of overall performance. Both materials presented optimal comprehensive properties when the cultivation period reached 30 days. Increasing the substrate length enhanced most of the material properties. The resulting density ranged from 0.13 to 0.16 g/cm³, which was higher than that of polystyrene foam. The contact angles of both materials exceeded 120°, whereas their elastic springback rates reached 50.2% and 43.2%, and their thermal conductivities were 0.049 W/m·K and 0.051 W/m·K, respectively. Additionally, thermogravimetric analysis revealed that both materials exhibited similar thermal degradation behavior and relatively high thermal stability. These findings align with those of previous studies on mycelium composites and indicate that the physical and mechanical properties of the materials are largely comparable to those of expanded polystyrene (EPS). In conclusion, the developed mycelium-based cushioning materials promote the efficient utilization of agricultural residues and hold promise as a sustainable alternative to EPS, offering broad application prospects in the transportation and packaging sectors. Full article

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33 pages, 1860 KiB

Open AccessReview

Biomimetic Design and Assessment via Microenvironmental Testing: From Food Packaging Biomaterials to Implantable Medical Devices

by Diana V. Portan, Athanasia Koliadima, John Kapolos and Leonard Azamfirei

Biomimetics 2025, 10(6), 370; https://doi.org/10.3390/biomimetics10060370 - 5 Jun 2025

Abstract

Biomaterials and biomedical devices interact with the human body at different levels. At one end of the spectrum, medical devices in contact with tissue pose risks depending on whether they are deployed on the skin or implanted. On the other hand, food packaging [...] Read more.

Biomaterials and biomedical devices interact with the human body at different levels. At one end of the spectrum, medical devices in contact with tissue pose risks depending on whether they are deployed on the skin or implanted. On the other hand, food packaging and associated material technologies must also be biocompatible to prevent the transfer of harmful molecules and contamination of food, which could impact human health. These seemingly unlinked domains converge into a shared need for the elaboration of new laboratory evaluation protocols that consider recent advances in biomaterials and biodevices, coupled with increasing legal restrictions on the use of animal models. Here, we aim to select and prescribe physiologically relevant microenvironment conditions for biocompatibility testing of novel biomaterials and biodevices. Our discussion spans (1) the development of testing protocols according to material classes, (2) current legislation and standards, and (3) the preparation of biomimetic setups that replicate the microenvironment, with a focus on the multidisciplinary dimension of such studies. Testing spans several characterization domains, beginning with chemical properties, followed by mechanical integrity and, finally, biological response. Biomimetic testing conditions typically include temperature fluctuations, humidity, mechanical stress and loading, exposure to body fluids, and interaction with multifaceted biological systems. Full article

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27 pages, 5640 KiB

Open AccessArticle

Holistic Education for a Resilient Future: An Integrated Biomimetic Approach for Architectural Pedagogy

by Lidia Badarnah

Biomimetics 2025, 10(6), 369; https://doi.org/10.3390/biomimetics10060369 - 5 Jun 2025

Abstract

The pressing need to address climate change and environmentally related challenges highlights the importance of reimagining educational approaches to equip students with the skills required for innovation and sustainability. This study proposes a novel holistic pedagogic framework for architectural education that integrates biomimicry, [...] Read more.

The pressing need to address climate change and environmentally related challenges highlights the importance of reimagining educational approaches to equip students with the skills required for innovation and sustainability. This study proposes a novel holistic pedagogic framework for architectural education that integrates biomimicry, systems thinking, and Bloom’s Revised Taxonomy to advance innovation, sustainability, and transformative learning. Developed through a triangulated methodological approach—combining reflective practitioner inquiry, design-based research, and conceptual model development—the framework draws from multiple theoretical perspectives to create a cognitively structured, interdisciplinary, and ecologically grounded educational model. Bloom’s Taxonomy provides a scaffold for learning progression, while the Function–Structure–Behavior (FSB) schema enhances the establishment of cross-disciplinary bridges to enable students to address complex design challenges. The framework is informed by insights from the literature and patterns observed in bio-inspired studios, student projects, and interdisciplinary workshops. These examples highlight how the approach supports systems thinking, ecological literacy, and ethical decision-making through iterative, experiential, and metacognitive learning. Rather than offering a fixed intervention, the framework is presented as a flexible, adaptable model that aligns learning outcomes with real-world complexity. It enables learners to navigate interdisciplinary knowledge, reflect critically on design processes and co-create regenerative solutions. By positioning nature as mentor, model, and measure, this pedagogic framework reimagines architectural education as a catalyst for sustainability and systemic change in the built environment. Full article

(This article belongs to the Special Issue Biomimetic Process and Pedagogy: Second Edition)

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12 pages, 3303 KiB

Open AccessArticle

Topology-Dependent Antifreeze Properties of Biomimetic Linear and Star-Shaped Peptoids

by Lei Feng, Liugen Xu, Junhao Wen, Minghai Zhao, Amjad Ali, Naushad Ahmad, Jianwei Lu and Li Guo

Biomimetics 2025, 10(6), 368; https://doi.org/10.3390/biomimetics10060368 - 4 Jun 2025

Abstract

Developing safe and efficient cryoprotectants is critical for effective cryopreservation in biomedical applications. Inspired by natural antifreeze proteins (AFPs), a series of linear and star-shaped peptoids featuring isopropanol side chains to mimic the amphiphilic characteristics of threonine were prepared. The effects of chain [...] Read more.

Developing safe and efficient cryoprotectants is critical for effective cryopreservation in biomedical applications. Inspired by natural antifreeze proteins (AFPs), a series of linear and star-shaped peptoids featuring isopropanol side chains to mimic the amphiphilic characteristics of threonine were prepared. The effects of chain length and molecular topology on antifreeze properties were systematically investigated. Both ice recrystallization inhibition (IRI) and ice crystal growth suppression improved with increasing chain length, and star-shaped peptoids exhibited superior performance. Notably, the star-shaped peptoid S-(A₆)₃ demonstrated excellent antifreeze activity and low cytotoxicity, highlighting its promise as a novel, non-toxic alternative to conventional cryoprotectants like DMSO. These findings provide valuable insight into the structure-property relationship of peptoids for cryopreservation applications. Full article

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16 pages, 3216 KiB

Open AccessArticle

UWB Indoor Localization Based on Artificial Rabbit Optimization Algorithm and BP Neural Network

by Chaochuan Jia, Can Tao, Ting Yang, Maosheng Fu, Xiancun Zhou and Zhendong Huang

Biomimetics 2025, 10(6), 367; https://doi.org/10.3390/biomimetics10060367 - 4 Jun 2025

Abstract

In the field of ultra-wideband (UWB) indoor localization, traditional backpropagation neural networks (BPNNs) are limited by their susceptibility to local minima, which restricts their ability to achieve global optimization. To overcome this challenge, this paper proposes a novel hybrid algorithm, termed ARO-BP, which [...] Read more.

In the field of ultra-wideband (UWB) indoor localization, traditional backpropagation neural networks (BPNNs) are limited by their susceptibility to local minima, which restricts their ability to achieve global optimization. To overcome this challenge, this paper proposes a novel hybrid algorithm, termed ARO-BP, which integrates the Artificial Rabbit Optimization (ARO) algorithm with a BPNN. The ARO algorithm optimizes the initial weights and thresholds of the BPNN, enabling the model to escape local optima and converge to a global solution. Experiments were conducted in both line-of-sight (LOS) and non-line-of-sight (NLOS) environments using a four-base-station configuration. The results demonstrate that the ARO-BP algorithm significantly outperforms traditional BPNNs. In LOS conditions, the ARO-BP model achieves a localization error of 6.29 cm, representing a 49.48% reduction compared to the 12.45 cm error of the standard BPNN. In NLOS scenarios, the error is further reduced to 9.86 cm (a 46.96% improvement over the 18.59 cm error of the baseline model). Additionally, in dynamic motion scenarios, the trajectory predicted by ARO-BP closely aligns with the ground truth, demonstrating superior stability. These findings validate the robustness and precision of the proposed algorithm, highlighting its potential for real-world applications in complex indoor environments. Full article

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18 pages, 2770 KiB

Open AccessArticle

Decentralized Multi-Robot Navigation Based on Deep Reinforcement Learning and Trajectory Optimization

by Yifei Bi, Jianing Luo, Jiwei Zhu, Junxiu Liu and Wei Li

Biomimetics 2025, 10(6), 366; https://doi.org/10.3390/biomimetics10060366 - 4 Jun 2025

Abstract

Multi-robot systems are significant in decision-making capabilities and applications, but avoiding collisions during movement remains a critical challenge. Existing decentralized obstacle avoidance strategies, while low in computational cost, often fail to ensure safety effectively. To address this issue, this paper leverages graph neural [...] Read more.

Multi-robot systems are significant in decision-making capabilities and applications, but avoiding collisions during movement remains a critical challenge. Existing decentralized obstacle avoidance strategies, while low in computational cost, often fail to ensure safety effectively. To address this issue, this paper leverages graph neural networks (GNNs) and deep reinforcement learning (DRL) to aggregate high-dimensional features as inputs for reinforcement learning (RL) to generate paths. Additionally, it introduces safety constraints through an artificial potential field (APF) to optimize these trajectories. Additionally, a constrained nonlinear optimization method further refines the APF-adjusted paths, resulting in the development of the GNN-RL-APF-Lagrangian algorithm. By combining APF and nonlinear optimization techniques, experimental results demonstrate that this method significantly enhances the safety and obstacle avoidance capabilities of multi-robot systems in complex environments. The proposed GNN-RL-APF-Lagrangian algorithm achieves a 96.43% success rate in sparse obstacle environments and 89.77% in dense obstacle scenarios, representing improvements of 59% and 60%, respectively, over baseline GNN-RL approaches. The method maintains scalability up to 30 robots while preserving distributed execution properties. Full article

(This article belongs to the Special Issue Data-Centric Engineering for Sustainable Future with AI and Human-in-the-Loop)

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17 pages, 3485 KiB

Open AccessArticle

Development of an Oblique Cone Dielectric Elastomer Actuator Module-Connected Vertebrate Fish Robot

by Taro Hitomi, Ryuki Sato and Aiguo Ming

Biomimetics 2025, 10(6), 365; https://doi.org/10.3390/biomimetics10060365 - 4 Jun 2025

Abstract

As a soft actuator for fish robots, an oblique cone dielectric elastomer actuator (DEA) module inspired by the structure of white muscles in fish was proposed in the authors’ previous study. However, a mathematical model of an oblique cone DEA was not established, [...] Read more.

As a soft actuator for fish robots, an oblique cone dielectric elastomer actuator (DEA) module inspired by the structure of white muscles in fish was proposed in the authors’ previous study. However, a mathematical model of an oblique cone DEA was not established, and designing a drive module that took into account its driving characteristics and passivity for integration into a fish robot remained a challenge. The purpose of this paper is to develop a vertebrate fish robot using multiple oblique cone DEA modules to achieve fish-like bending capability. First, an oblique cone DEA module was modeled for the design of a fish robot. The relationships among bending angle, blocking torque, driving voltage, and design parameters were established and confirmed by comparing the calculated and experimental results. Based on the modeling results, we designed an oblique cone DEA module-connected vertebrate fish robot. Finally, the experimental results of the fabricated fish robot demonstrated that the model-based design enabled flexible body swinging and swimming through a multiple-module-connected vertebrate structure. Full article

(This article belongs to the Section Locomotion and Bioinspired Robotics)

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20 pages, 14992 KiB

Open AccessArticle

A Lightweight Bioinspired SMA-Based Grasping Mechanism for Flapping Wing MAVs

by Ahmad Hammad, Mehmet Süer and Sophie F. Armanini

Biomimetics 2025, 10(6), 364; https://doi.org/10.3390/biomimetics10060364 - 4 Jun 2025

Abstract

This study presents a novel, bioinspired perching mechanism designed to enhance the landing and takeoff capabilities of flapping wing micro aerial vehicles (FWMAVs). Drawing inspiration from the human hand, the lightweight gripper integrates a compliant claw structure actuated by shape memory alloys (SMAs) [...] Read more.

This study presents a novel, bioinspired perching mechanism designed to enhance the landing and takeoff capabilities of flapping wing micro aerial vehicles (FWMAVs). Drawing inspiration from the human hand, the lightweight gripper integrates a compliant claw structure actuated by shape memory alloys (SMAs) that mimic muscle movement. These SMA springs act as compact, lightweight substitutes for traditional actuators like motors or solenoids. The mechanism operates via short electrical impulses that trigger both opening and closing motions. A detailed design process was undertaken to optimize phalange lengths for cylindrical grasping and to select appropriate SMAs for reliable performance. Weighing only 50 g, the gripper leverages the high power-to-weight ratio and flexibility of SMAs, with the springs directly embedded within the phalanges to reduce size and mass while preserving high-force output. Experimental results demonstrate fast actuation and a grasping force of approximately 16 N, enabling the gripper to hold objects of varying shapes and sizes and perform perching, grasping, and carrying tasks. Compared to existing solutions, this mechanism offers a simpler, highly integrated structure with enhanced miniaturization and adaptability, making it especially suitable for low-payload MAV platforms like FWMAVs. Full article

(This article belongs to the Special Issue Bio-Inspired Robotics and Applications 2025)

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17 pages, 3934 KiB

Open AccessArticle

A Piezoelectric Sensor Based on MWCNT-Enhanced Polyvinyl Chloride Gel for Contact Perception of Grippers

by Qiyun Zhong, Qingsong He, Diyi Liu, Xinyu Lu, Siyuan Liu, Yuze Ye and Yefu Wang

Biomimetics 2025, 10(6), 363; https://doi.org/10.3390/biomimetics10060363 - 3 Jun 2025

Abstract

In contrast to traditional hydrogels, which are susceptible to water evaporation and structural degradation, non-hydrogel materials are engineered for superior stability and consistent performance. Here, we report an innovative piezoelectric polyvinyl chloride/multi-walled carbon nanotube polymer gel (PVC/MWCNT polymer gel, PMPG) with exceptional linearity [...] Read more.

In contrast to traditional hydrogels, which are susceptible to water evaporation and structural degradation, non-hydrogel materials are engineered for superior stability and consistent performance. Here, we report an innovative piezoelectric polyvinyl chloride/multi-walled carbon nanotube polymer gel (PVC/MWCNT polymer gel, PMPG) with exceptional linearity (as low as 1.31%), high sensitivity (50–310.17 mV), rapid response (172–189 ms), and thermal stability. Under strain induction, ordered rearrangement of dipoles in PMPG and the enhancement of MWCNTs generate a potential difference. Increasing MWCNT content enhances output voltage, sensitivity, conductivity, maximum stress, Young’s modulus, and toughness, while reducing nonlinear error. Higher dibutyl adipate (DBA) content increases output voltage and slightly improves sensitivity but decreases mechanical strength. The optimal PMPG (PVC:DBA = 1:5, 1 wt% MWCNTs) exhibited outstanding performance. It exhibits a nonlinear error as low as 1.31%, a conductivity of 25.4 μS/cm, an 80% compressive strain tolerance (273 kPa stress), and dimensional stability for 90 days in air. By integrating PMPG with machine learning algorithms, soft robotic grippers gain advanced contact perception capabilities, enabling applications in medicine, rescue, exploration, and other fields requiring fine manipulation and adaptability. This work highlights PMPG’s potential as a stable, high-performance material for soft robotics and beyond. Full article

(This article belongs to the Special Issue Bioinspired Nature-Based Adhesives: Design and Applications)

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21 pages, 32153 KiB

Open AccessArticle

Inversion of Biological Strategies in Engineering Technology: A Case Study of the Underwater Soft Robot

by Siqing Chen, He Xu, Xueyu Zhang, Tian Jiang and Zhen Ma

Biomimetics 2025, 10(6), 362; https://doi.org/10.3390/biomimetics10060362 - 3 Jun 2025

Abstract

Bio-inspired design, a paradigm-shifting methodology that translates evolutionary mechanisms into engineering solutions, has established itself as a cornerstone for pioneering innovation in multifaceted technological systems. Despite its promise, the inherent complexity of biological systems and interdisciplinary knowledge gaps hinder the effective translation of [...] Read more.

Bio-inspired design, a paradigm-shifting methodology that translates evolutionary mechanisms into engineering solutions, has established itself as a cornerstone for pioneering innovation in multifaceted technological systems. Despite its promise, the inherent complexity of biological systems and interdisciplinary knowledge gaps hinder the effective translation of biological principles into practical engineering solutions. This study introduces a structured framework integrating large language models (LLMs) with a function–behavior–characteristic–environment (F-B-C-E) paradigm to systematize biomimetic design processes. We propose a standardized F-B-C-E knowledge model to formalize biological strategy representations, coupled with a BERT-based pipeline for automated inversion of biological strategies into engineering applications. To optimize strategy selection, a hybrid decision-making methodology combining VIKOR multi-criteria analysis and rank correlation is developed. The framework’s functional robustness is validated via aquatic robotic system implementations, wherein three biomimetic propulsion modalities—oscillatory caudal propulsion, pulsed hydrodynamic thrust generation, and autonomous peristaltic locomotion—demonstrate quantifiable enhancements in locomotion efficiency and environmental adaptability metrics. These results underscore the robustness of the proposed inversion methodology in resolving intricate engineering problems through systematic biomimetic translation. Full article

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29 pages, 3350 KiB

Open AccessArticle

A Novel Black Widow Optimization Algorithm Based on Lagrange Interpolation Operator for ResNet18

by Peiyang Wei, Can Hu, Jingyi Hu, Zhibin Li, Wen Qin, Jianhong Gan, Tinghui Chen, Hongping Shu and Mingsheng Shang

Biomimetics 2025, 10(6), 361; https://doi.org/10.3390/biomimetics10060361 - 3 Jun 2025

Abstract

Hyper-parameters play a critical role in neural networks; they significantly impact both training effectiveness and overall model performance. Proper hyper-parameter settings can accelerate model convergence and improve generalization. Among various hyper-parameters, the learning rate is particularly important. However, optimizing the learning rate typically [...] Read more.

Hyper-parameters play a critical role in neural networks; they significantly impact both training effectiveness and overall model performance. Proper hyper-parameter settings can accelerate model convergence and improve generalization. Among various hyper-parameters, the learning rate is particularly important. However, optimizing the learning rate typically requires extensive experimentation and tuning, as its setting is often dependent on specific tasks and datasets and therefore lacks universal rules or standards. Consequently, adjustments are generally made through trial and error, thereby making the selection of the learning rate complex and time-consuming. In an attempt to surmount this challenge, evolutionary computation algorithms can automatically adjust the hyper-parameter learning rate to improve training efficiency and model performance. In response to this, we propose a black widow optimization algorithm based on Lagrange interpolation (LIBWONN) to optimize the learning rate of ResNet18. Moreover, we evaluate LIBWONN’s effectiveness using 24 benchmark functions from CEC2017 and CEC2022 and compare it with nine advanced metaheuristic algorithms. The experimental results indicate that LIBWONN outperforms the other algorithms in convergence and stability. Additionally, experiments on publicly available datasets from six different fields demonstrate that LIBWONN improves the accuracy on both training and testing sets compared to the standard BWO, with gains of 6.99% and 4.48%, respectively. Full article

(This article belongs to the Section Biological Optimisation and Management)

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35 pages, 11695 KiB

Open AccessArticle

Polymorphism in Glu-Phe-Asp Proteinoids

by Panagiotis Mougkogiannis and Andrew Adamatzky

Biomimetics 2025, 10(6), 360; https://doi.org/10.3390/biomimetics10060360 - 3 Jun 2025

Abstract

Glu-Phe-Asp (GFD) proteinoids represent a class of synthetic polypeptides capable of self-assembling into microspheres, fibres, or combinations thereof, with morphology dramatically influencing their electrical properties. Extended recordings and detailed waveforms demonstrate that microspheres generate rapid, nerve-like spikes, while fibres exhibit consistent and gradual [...] Read more.

Glu-Phe-Asp (GFD) proteinoids represent a class of synthetic polypeptides capable of self-assembling into microspheres, fibres, or combinations thereof, with morphology dramatically influencing their electrical properties. Extended recordings and detailed waveforms demonstrate that microspheres generate rapid, nerve-like spikes, while fibres exhibit consistent and gradual variations in voltage. Mixed networks integrate multiple components to achieve a balanced output. Electrochemical measurements show clear differences. Microspheres have a low capacitance of

1.926 \pm 5.735

μ

F. They show high impedance at

6646.282 \pm 178.664

Ohm. Their resistance is low, measuring 15,830.739 ± 652.514 m

Ω

. This structure allows for quick ionic transport, leading to spiking behaviour. Fibres show high capacitance (

9.912 \pm 0.171

μ

F) and low impedance (

209.400 \pm 0.286

Ohm). They also have high resistance (163,067.613 ± 9253.064 m

Ω

). This combination helps with charge storage and slow potential changes. The 50:50 mixture shows middle values for all parameters. This confirms that hybrid electrical properties have emerged. The differences come from basic structural changes. Microspheres trap ions in small, round spaces. This allows for quick release. In contrast, fibers spread ions along their length. This leads to slower wave propagation. In mixed systems, diverse voltage zones emerge, suggesting cooperative dynamics between morphologies. This electrical polymorphism in simple proteinoid systems may explain complexity in biological systems. This study shows that structural polymorphism in GFD proteinoids affects their electrical properties. This finding is significant for biomimetic computing and sheds light on prebiotic information-processing systems. Full article

(This article belongs to the Section Biomimetic Surfaces and Interfaces)

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22 pages, 25396 KiB

Open AccessArticle

Biomimetic Visual Information Spatiotemporal Encoding Method for In Vitro Biological Neural Networks

by Xingchen Wang, Bo Lv, Fengzhen Tang, Yukai Wang, Bin Liu and Lianqing Liu

Biomimetics 2025, 10(6), 359; https://doi.org/10.3390/biomimetics10060359 - 3 Jun 2025

Abstract

The integration of in vitro biological neural networks (BNNs) with robotic systems to explore their information processing and adaptive learning in practical tasks has gained significant attention in the fields of neuroscience and robotics. However, existing BNN-based robotic systems cannot perceive the visual [...] Read more.

The integration of in vitro biological neural networks (BNNs) with robotic systems to explore their information processing and adaptive learning in practical tasks has gained significant attention in the fields of neuroscience and robotics. However, existing BNN-based robotic systems cannot perceive the visual environment due to the inefficiency of sensory information encoding methods. In this study, we propose a biomimetic visual information spatiotemporal encoding method based on improved delayed phase encoding. This method transforms high-dimensional images into a series of pulse sequences through convolution, temporal delay, alignment, and compression for BNN stimuli. We conduct three stages of unsupervised training on in vitro BNNs using high-density microelectrode arrays (HD-MEAs) to validate the potential of the proposed encoding method for image recognition tasks. The neural activity is decoded via a logistic regression model. The experimental results show that the firing patterns of BNNs with different spatiotemporal stimuli are highly separable in the feature space. After the third training stage, the image recognition accuracy reaches 80.33% ± 7.94%, which is 13.64% higher than that of the first training stage. Meanwhile, the BNNs exhibit significant increases in the connection number, connection strength, and inter-module participation coefficient after unsupervised training. These results demonstrate that the proposed method significantly enhances the functional connectivity and cross-module information exchange in BNNs. Full article

(This article belongs to the Section Bioinspired Sensorics, Information Processing and Control)

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